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About this paper
Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han and 2 more
KBAlign introduces a self-supervised framework for adapting Retrieval-Augmented Generation (RAG) systems to specific textual knowledge bases (KBs) without external supervision or expensive large models. It utilizes multi-grained self-annotation and iterative self-verification to efficiently align LLMs with KB content, yielding substantial improvements in question answering accuracy across diverse tasks while significantly reducing adaptation costs.
Key insights
- Self-supervised learning, through multi-grained annotation and iterative self-verification, can effectively enable LLMs to autonomously grasp and align with specific KBs, akin to human 'self-study'.
- The framework significantly reduces the cost and time of domain adaptation compared to methods requiring external supervision (e.g., GPT-4o fine-tuning) while achieving competitive performance.
- Tailored annotation strategies (short-dependency for local facts and long-dependency for global, multi-hop information) are crucial for optimizing performance across different types of knowledge-based tasks.
Summary by alphaXiv.
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